论文标题
域自适应多对象跟踪的技巧袋
Bag of Tricks for Domain Adaptive Multi-Object Tracking
论文作者
论文摘要
在本文中，介绍了SIA_TRACK，由SI Analytics的研究团队开发。所提出的方法是由逐个检测范式下的预先存在的检测器和跟踪器构建的。我们使用的跟踪器是一个在线跟踪器，它仅将新收到的检测与现有跟踪链接在一起。我们方法的核心部分是对象检测器的训练程序，其中合成和未标记的实际数据仅用于培训。为了最大程度地提高真实数据的性能，我们首先建议使用使用合成数据集训练的模型为真实数据生成不完美标签的伪标记。之后，将模型汤计划应用于迭代伪标记期间产生的聚集重量。此外，跨域混合采样还有助于提高实际数据的检测性能。我们的方法SIA_TRACK在BMTT 2022 Challenge的Motsynth2Mot17曲目上排名第一。该代码可在https://github.com/sianalytics/bmtt2022_sia_track上找到。
In this paper, SIA_Track is presented which is developed by a research team from SI Analytics. The proposed method was built from pre-existing detector and tracker under the tracking-by-detection paradigm. The tracker we used is an online tracker that merely links newly received detections with existing tracks. The core part of our method is training procedure of the object detector where synthetic and unlabeled real data were only used for training. To maximize the performance on real data, we first propose to use pseudo-labeling that generates imperfect labels for real data using a model trained with synthetic dataset. After that model soups scheme was applied to aggregate weights produced during iterative pseudo-labeling. Besides, cross-domain mixed sampling also helped to increase detection performance on real data. Our method, SIA_Track, takes the first place on MOTSynth2MOT17 track at BMTT 2022 challenge. The code is available on https://github.com/SIAnalytics/BMTT2022_SIA_track.
